the use of quantitative methods in research on selected behavioral

Studia Ekonomiczne. Zeszyty Naukowe
Uniwersytetu Ekonomicznego w Katowicach
ISSN 2083-8611
Nr 247 · 2015
Informatyka i Ekonometria 4
Katarzyna Warzecha
University of Economics in Katowice
Faculty of Management
Department of Econometrics
warzecha@ue.katowice.pl
THE USE OF QUANTITATIVE METHODS
IN RESEARCH ON SELECTED BEHAVIORAL
ADDICTIONS OF YOUNG PEOPLE
Summary: The aim of this article is to present the use of quantitative methods in research
on selected behavioral addictions of young people, and in particular in research on pathological use of the Internet by Silesian young people. Hellwig’s taxonomic measure of development was used in the research which allowed the arrangement of districts in Silesia province as regards the availability of the Internet access for young people in schools and the
creation of ranking of districts with schools equipped with computers in the highest degree.
The use of Czekanowki’s method allowed the identification of groups of districts with similar level of the availability of the Internet access for young people in schools. In the conducted analyses, the hypothesis whether the territorial location of researched units influences the availability of the Internet access will be verified. For this purpose the spatial
statistics will be used – Moran’s measures of local and global autocorrelation. The study
will be based on data from the Local Data Bank of Polish CSO (GUS) from 2012 and from
own survey study conducted in selected cities of Silesia Province. The Internet addiction
will be examined with the use of K. Young’s test – Internet Addiction Test (IAT). The calculations will be made in R Cran and Microsoft Excel.
Keywords: behavioral addictions, Internet, young people, spatial statistics, Czekanowski’s
diagram, K. Young’s test.
Introduction
Addictive disorder is the term associated the most with the addiction to
psychoactive substances such as: alcohol, nicotine, medicines, drugs or so called
“designer drugs” which gain more and more popularity among teenagers every
day. The beginning of 21st century is called “the era of new addictions”. The
122
Katarzyna Warzecha
term “behavioral addiction” or “action addiction” is classified as behavioral disorder of addictive nature which is not connected with taking psychoactive substances and its main aim is to perform specific actions in pathological way in order to put the individual in a better mood, improve his lowered self – assessment
or gloomy state of mind [Jaraczewska, Adamczyk-Zientara, 2015, p. 53]. The
individual is not able to control this addictive behavior despite the fact that it can
disturb many spheres of his proper functioning. The main reasons of existence of
such addictions are the changes of civilization, fast pace of life and more and
more consumerist attitude towards life, stressful life conditions and accompanying negative emotions. The modern individual seeks fast pleasure, immediate
gratification and he encounters difficulty in controlling his own impulses [Ogińska-Bulik, 2010]. The behavioral addictions are among others: spending time on
the Internet (so called: webaholism), gaming, participation in games of chance
(pathological gambling), workaholism, shopping addiction, sex addiction, obsessive and compulsive eating or exercising [Woronowicz, 2012].
According to the specialist literature and research conducted in Poland and
all over the world, it is widely known that last years are significant in the increase of troublesome behaviors among children and teenagers. For some behaviors (e.g. aggression, violence, crime, drug addiction or alcoholism) there are
available reliable methods of measuring them and effective and practical solutions (so called good practices). But when it comes to new troublesome behaviors among children and teenagers (e.g. computer games or Internet addiction,
troublesome use of social media − social webs and mobile phones, gambling,
lobbying, cyberbullying, using drastic diets, juvenile prostitution and the phenomenon of sponsorship) there are not enough ready-to-use research tools which
could help in fast identification of children and teenagers afflicted with these
problems [Jarczyńska, 2014, p. 8-9].
In the further examination the troublesome behaviors among children and teenagers connected with the use of computer and spending time on the Internet for many
hours will be described and the quantitative methods used to research the availability
of the Internet access for young people in Silesian schools will be presented.
Along with the occurrence of the Internet (at the end of 60s, 20th century)
and the development of computerization it is possible to observe (especially
among young people) that traditional communication means such as: television,
radio, press or books are superseded by so called new media i.e. the Internet or
mobile phones. The Internet is the modern tool used for the entertainment and
interesting way of spending leisure time for modern young people, it facilitates
life in many of its aspects but uncontrolled use of this medium may result in se-
The Use off Quantitative Methods…
123
vere distuurbances in the
t areas of mental and social functtioning. Thee computer
and the Innternet access may addictt on the samee level with alcohol
a
or otther stimulants. Thee Internet is also
a a very valuable source of knowleedge for youung people,
on conditiion that the contents
c
to which
w
teenageers have acceess are contrrolled.
1. The usse of taxonomic methods in the research
r
off the availaability
of Intternet accesss for young people in
n schools
Hellwig’’s measure of developm
ment and Czekanows
C
ski’s diagraam
In thee research off the availabiility of Intern
net access forr Silesian youung people
in schoolss the division of researched area for thee districts waas adopted. Siilesia province with the territory of 12 333 km
k 2 constitutees 3.9% of thhe state territtory. In administrativve division acccording to the
th Nomenclaature of Terriitorial Units ffor Statistical Purpooses (NTS4) 17 districtss and 19 citties with disstrict status (municipal
districts) are
a distinguisshed and pressented in Figu
ure 1 [www 1;
1 www 2].
The level
l
of availlability of thee Internet acccess among young
y
people in schools
of Silesiann districts waas characterizzed with the use
u of variabbles defining its various
aspects (vvariables in division intoo stimulants and destimuulants are prresented in
Table 1). On the basis of chosen vaariables the av
vailability off the Internet access was
characterizzed with thee use of Helllwig’s syntheetic taxonomiic measure oof development (the description of
o the measurre: Zeliaś (ed
d.) [2000]; Zeeliaś (ed.) [19989]; Młodak [20066]; Warzechaa [2009]). Heellwig’s meassure allows the
t arrangem
ment of the
tested unitts (districts) according
a
to the
t researched
d phenomenoon.
Fig. 1. Sileesia Province with the divission for districcts in 2012
Source: [www
w 2].
Katarzyna Warzecha
124
Destimulants were converted into stimulants, subsequently all of the variables were normalized (due to the fact that our diagnostic variables are in different measure units they cannot be aggregated directly) according to formula (1)
with the use of Perkal’s Method:
(1)
( )
where: is the mean value of xj variable; S(xj) is the standard deviation of xj variable.
Subsequently, the Hellwig’s taxonomic measure of development (zi) was
calculated, i.e. the synthetic measure of development which takes the values
within the range [0,1]. The higher value of this indicator the more favorable position of the object. In extreme cases, the values from outside the mentioned
scope may appear which is a signal that the level of development of a given unit
is drastically different from other units.
Table 1. Variables characterizing the level of Internet access for young people in schools
Nature
of variable
Variable and its description
Coeficient
of variation CV
[in %]
2012
X1 – number of students per 1 computer with Internet access
which is assigned for use by students in upper secondary schools
X2 – number of students per 1 computer with Internet access
which is assigned for use by students in secondary schools
X3 – number of students per 1 computer with Internet access
which is assigned for use by students in primary schools
X4 − share of upper secondary schools equipped with
the computers with Internet access assigned for use by students
X5 − share of secondary schools equipped with the computers
with Internet access assigned for use by students
Destimulant
24.2
Destimulant
20.5
Destimulant
22.2
Stimulant
19.6
Stimulant
10.9
Source: Own elaboration and calculations on the basis of data from the Local Data Bank of Polish CSO (GUS)
[accessible at www 3].
Hellwig’s taxonomic measure of development was calculated according to
the formula (2):
1
(2)
where: di0 is the distance of i-object from the model object. The value of d0 is indicated with the formula (3):
1
2·
1
1
(3)
The Use off Quantitative Methods…
125
On thhe basis of thhe data preseented in Table 2 and Figuure 2 it is visiible that in
Silesian districts
d
rankking as regaards the avaiilability of the
t Internet access for
young peoople in schoools in 2012 leading
l
posittions were heeld by districcts: częstochowski, gliwicki andd m. Piekary Śląskie and closing positions were hheld by m.
Jastrzębiee-Zdrój, m. Sosnowiec
S
annd m. Ruda Śląska.
Ś
Table 2. Thhe values of Heellwig’s syntheetic measure forr districts in Sillesia Province iin 2012
District
częstochow
wski
gliwicki
m. Piekary Śląskie
będziński
pszczyński
m. Dąbrow
wa Górnicza
m. Bielsko--Biała
myszkowskki
m. Tychy
m. Rybnik
tarnogórskii
m. Chorzów
w
kłobucki
m. Gliwice
lubliniecki
m. Żory
mikołowski
cieszyński
zi
0.728
0.616
0.594
0.528
0.516
0.484
0.467
0.466
0.451
0.444
0.442
0.434
0.424
0.414
0.405
0.378
0.356
0.348
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
District
bieruń
ńsko-lędziński
m. Siemianowice Śl.
bielsk
ki
zawieerciański
rybnicki
m. Kaatowice
żywieecki
m. Św
więtochłowice
wodzzisławski
racibo
orski
m. Czzęstochowa
m. By
ytom
m. Mysłowice
M
m. Jaw
worzno
m. Zaabrze
m. Ru
uda Śląska
m .So
osnowiec
m. Jastrzębie-Zdrój
zi
0.3388
0.3333
0.331
0.301
0.2966
0.2900
0.2799
0.2744
0.2677
0.2555
0.2288
0.2199
0.2155
0.1877
0.0966
0.0022
-0.0223
-0.084
Rank
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Source: Own elaboration.
Fig. 2. The distance of ressearched districcts from model according to Hellwig’s
H
methhod in 2012
Source: Own elaboration.
126
Katarzyna Warzecha
Czekanowski’s method belongs to the group of multidimensional analysis
methods (the oldest taxonomic method published in 1909 by Czekanowski) and
its graphic demonstration is Czekanowski’s diagram. The procedure of this
method was described in Heffner, Gibas [2007, p. 55]. Czekanowski’s method
allows the identification of the groups of districts with the similar level of the
availability of the Internet access among students in schools. If the groups of districts partially correspond to each other, the division is made on the basis of the
shortest Euclidean distance of inconclusively defined district and other districts
from possible groups of its affiliation.
The method of arithmetic means was used in order to establish which of the
tested characteristics possessed the decisive influence on the division of similar
groups of districts made with Czekanowski’s method.
The above mentioned method is based on the calculations of the arithmetic
means from the primary data: for all of the Xi variables and all of the districts
(general mean), and subsequently the districts and Xi variables taken into consideration in particular groups of similar districts (group means). Next step is the
calculation of quotient of group means and general means for every variable. If
given |quotient| > 1 it proves that the particular variable (characteristic) is dominant in considered group and if |quotient| < 1 it informs about the lack of particular characteristic in this cluster [Heffner, 2007, p. 73].
In Table 3, for every group of district clusters which are similar as regards
the availability of the Internet access among young people in schools, the quotient values which exceeded 1 were written in bold face, whereas the value of the
characteristic which influences the particular group in the highest degree was
additionally located on the darker background.
On the basis of Czekanowski’s ordered diagram (Figure 3) it is possible to
identify 5 groups of districts with similar level of the availability of the Internet
access among young people in schools.
1. Group of districts: bielski, m. Gliwice, m. Rybnik, pszczyński, m. Bielsko-Biała, m. Dąbrowa-Górnicza, będziński, m. Chorzów, cieszyński.
2. Group of districts: m. Mysłowice, m. Bytom, wodzisławski, m. Częstochowa.
3. Group of districts: żywiecki, zawierciański, kłobucki.
4. Group of districts: lubliniecki, m. Tychy, tarnogórski.
5. Group of districts: mikołowski, m. Katowice, m. Jaworzno, m. Żory, m. Siemianowice Śląskie.
The remaining districts (raciborski, bieruńsko-lędziński, m. Świętochłowice, gliwicki, częstochowski, myszkowski, m. Piekary, rybnicki, m. Zabrze,
m. Jastrzębie-Zdrój, m. Ruda Śląska, m. Sosnowiec) do not show sufficiently
close similarity with other districts.
The Use off Quantitative Methods…
127
Fig. 3. Czeekanowski’s ordered
o
diagraam for x1-x5 vaariables, 2012
Source: Own elaboration.
On thhe basis of thhe data preseented in Table 3 in 2012 in group I w
which consists of ciities with district status, namely: m.. Gliwice, m.
m Rybnik, m
m. Bielsko-Biała, m.
m Dąbrowa--Górnicza, m.
m Chorzów
w and distriicts such as: bielski,
pszczyńskki, będziński, cieszyński, a percentage share of uppper secondaary schools
equipped with the com
mputers withh Internet acccess assigneed for use bby students
(x4 variabble) and a percentage
p
shhare of seco
ondary schoools equippedd with the
computerss with Internnet access asssigned for use
u by studeents (x5 variaable) were
both on thhe slightly hiigher level thhan the mean
n level in Sileesia Provincee.
Table 3. The
T comparisoon of group meeans with gen
neral means, x1-x5 variables,, 2012
Group num
mber
Quotient of meeans for variabless:
X1
X2
X3
X4
X5
1
0.933
0.997
0.95
0
1.04
1.01
2
1.211
1.112
1.09
1
0.87
1.07
3
1.100
0.992
0.71
0
0.94
0.89
4
0.955
0.883
0.86
0
0.84
1.11
5
0.900
1.001
1.30
1
0.96
1.00
Source: Own calculations on the
t basis of data from
f
the Local Daata Bank of Polissh CSO (GUS).
128
Katarzyna Warzecha
In 2012, in group II which consists of cities with district status, namely:
m. Mysłowice, m. Bytom, m. Częstochowa and the wodzisławski district, x1, x2, x3
variables obtained higher level than the mean level in Silesia Province. And they
are respectively: x1 variable (students per 1 computer with Internet access which
is assigned for use by students in upper secondary schools − 21% higher than the
mean level of the whole province), x2 variable (students per 1 computer with
Internet access which is assigned for use by students in secondary schools −
12% higher than the mean level of the whole province) and x3 variable (students
per 1 computer with Internet access which is assigned for use by students in
primary schools − 9% higher than the mean level of the whole province).
It proves the unfavorable situation for this group of districts compared with other
identified groups because all of the variables with the higher level than the mean
level of the whole province were destimulants. Only the x5 variable, namely percentage share of secondary schools equipped with the computers with Internet
access assigned for use by students, could be found above the mean level of the
province (exactly 7% higher than the mean level of the province) and it proved
the favorable situation in this group of districts. The high level of variables with
the negative influence on the availability of the Internet access among young
people may suggest that group II is one of the “worst”.
In group III, consisting only of districts such as: żywiecki, zawierciański,
kłobucki, there was the smallest number of students per 1 computer with Internet
access which is assigned for use by students in primary schools (29% below the
mean level in the province) which proves the favorable situation of this group of
districts but at the same time it proves unfavorable situation as regards the significance of x5 variable (11% below the mean level of the province). Therefore,
those districts possessed the smallest percentage share of secondary schools
equipped with computers with the Internet access assigned to use by students.
Group IV, consisting of the city with district status, namely m. Tychy and
the districts such as: tarnogórski and lubliniecki turned out to have the best position comparing to other researched groups of districts. In this group, x1-x3 variables could be found significantly below the mean level of the province which
means that there was the smallest number of students per 1 computer with the
Internet access assigned to use by students in upper secondary schools, secondary schools and primary schools and x5 variable (percentage share of secondary
schools equipped with the computers with Internet access assigned for use by
students) could be found significantly above the mean level of the province.
In group V, consisting of the cities with district status such as: m. Katowice,
m. Jaworzno, m. Żory, m. Siemianowice Śląskie and mikołowski district, there was
the highest number of students per 1 computer with the Internet access assigned to
The Use of Quantitative Methods…
129
use by students in primary schools − significantly above the mean level of province
(exactly 30% above the mean level of province), whereas the smallest number of
students per 1 computer with the Internet access assigned to use by students in upper secondary schools − 10% below the mean level in the province.
2. The use of spatial statistics in the research of the availability
of Internet access for young people in schools
Spatial statistics: global and local measures
In the further part of this study the hypothesis whether the territorial location of researched units influences the availability of the Internet access will be
verified. For this purpose the spatial statistics will be used – Moran’s measures
of local and global autocorrelation. The calculations will be made in R Cran and
Microsoft Excel.
Spatial statistics are one of the ways of testing the existence of the spatial
autocorrelation, whereas the spatial autocorrelation indicates that nearby geographical observations are more similar to each other than distant observations
[Kopczewska, 2011, p. 69]. If positive autocorrelation occurs in certain area, this
means that there is a spatial cluster with high or low values of observed variables. It indicates that areas with high values of a given variable are clustered
with other high value areas, and the areas with low values of a variable are clustered with other low value areas. In case of negative autocorrelation, the high
value areas are neighboring to low value areas and vice versa, creating the alternating areas with dissimilar values of a variable (so called checkerboard). The
lack of spatial autocorrelation indicates spatial randomness, which means that
the high and low values of observed variable are distributed independently
[cf. Suchecki, 2010, p. 103].
The measures of global autocorrelation (the Moran’s I statistics − the single
number indicator of autocorrelation or the general similarity of districts) and local autocorrelation (local Moran’s Ii statistics − the statistics calculated for every
area and answering the question whether the given area is similar/dissimilar to
the neighboring areas) were taken into consideration in this research
[cf. Kopczewska, 2011, p. 69].
The global Moran’s I statistics is used to test the existence of global spatial
autocorrelation and it is defined in formula 4:
Katarzyna Warzecha
130
I=
n⋅ ∑∑
w (x − x)(xj − x)
i
j ij i
∑∑ w ⋅ ∑(x − x)
2
i
j
ij
i
(4)
i
where: xi, xj are the values of variable in spatial unit i, j; x is the mean value of
variable for all of the spatial units; n is the total number of spatial units that are
included in the study; wij is an element of spatial weights matrix.
The basic element of spatial analysis is determining the structure of neighborhood with the use of spatial weights. The spatial weights matrix can be defined by two categories of neighbors: contiguity-based neighbors and distance-based neighbors. In the research, it is assumed that mutual interactions between
districts occur if they have common borders. Therefore, the binary matrix is created (taking the value of 1 if the districts are adjacent or taking the value of 0 if
the districts are not neighboring). Next, the matrix created in such a manner have
to be row-standardized, that allows the comparison of results of various areas
that have been analyzed.
If the values of Moran’s I statistics are positive and significant, they indicate the existence of positive spatial autocorrelation, i.e. the similarity between
the analyzed objects in defined distance d. The negative values of Moran’s I statistics indicate the negative autocorrelation and they refer to so called hot spots,
namely the spots with markedly favored values (high or low). The values of statistics which equal 0 indicate the lack of spatial autocorrelation.
The graphic presentation of the Global Moran’s I statistics is the scatter plot
(Moran scatter plot), which shows local spatial associations (clusters), outliers
and spatial instabilities [Anselin, 1995, p. 93-115]. The graph presents a standardized variable (here Hellwig’s measure of development) in the x-axis versus
the spatial lag of that standardized variable in the y-axis.
The graph divides into quarters in relation to point (0,0). The points situated in
the bottom left quarter (LL) indicate the positive spatial autocorrelation and the low
values of variable. The points situated in the top right quarter (HH) indicate the positive spatial autocorrelation and the high values of variable. The points situated in the
top left quarter (HL) indicate the negative spatial autocorrelation and the high values
of variable whereas the points situated in the bottom right quarter (LH) indicate the
negative spatial autocorrelation and the low values of variable.
In the described Moran scatter plot the direction coefficient of linear regression
is the Global Moran’s I statistics (and it is interpreted as the intensity of the association between the degree of access to the Internet by young people in schools and
geographical location of districts − therefore, it informs what percentage of the
The Use off Quantitative Methods…
131
tested pheenomenon occcurs in particcular area − district
d
and results from thhe values of
this phenoomenon in neighboring disstricts) [cf. Ko
opczewska, 2011,
2
p. 72-755].
The local Morann Ii statistics allows the identification
i
n of spatial accumulation and measures whether
w
the given
g
spatiall unit – disttrict is surroounded by
neighboring units withh similar or dissimilar vaalues of the tested variabble in relation to ranndom distribbution of these values in space and itt is determinned by formula 5 [K
Kopczewska, 2011, p. 90]]:
n
Ii =
( xi − x )∑ w ( x
i =1
j
− x)
ij
(5)
( xi − x )2
∑
n
i =1
o
row − sttandardized sppatial weightss matrix W,
where: wij are the elemeents of first order
the other elements
e
of thhe formula aree defined as in
n the Global Moran’s
M
I stattistics.
If thee standardizeed Local Moran Ii statistiics takes signnificantly neggative values, it inddicates that thhe object i is surrounded by the spatiaal units − disstricts with
significanntly dissimilaar values of the
t tested varriable, whichh should be iinterpreted
as the neggative autocoorrelation. When
W
the Ii staatistics takess the significaantly positive valuees, it indicatees that the obbject i is surrrounded by thhe similar neeighboring
spatial unnits − districts and we deaal with the positive autoccorrelation annd clustering of the spatial unitss.
The Figure 4 bellow is the grraphic presen
ntation of Global
G
Morann’s I statistics − thhe scatter plot
and nonp
which shows locaal spatial associations
a
standardizzed observatiions.
Globbal Moran’s I stattistics scatter plott
n
The graaph of Local Moraan’s significant sttatistics, 2012
Fig. 4. Thee graphs of Gllobal Moran’ss I statistics an
nd Local Moraan’s significannt statistics
in 2012
2
Source: Own elaboration.
Katarzyna Warzecha
132
The conducted study presented protruding observations, non-standardized,
i.e. the districts which protrude significantly from other districts (so-called hot
spots). In 2012, protruding districts were as follows: m. Sosnowiec, m. Jastrzębie-Zdrój, m. Ruda Śląska, m. Częstochowa, zawierciański, częstochowski.
The calculated value of Global Moran’s I statistics 0.085 (p-value = 0.189 >
0.05) indicates that there was no statistically significant similarity between provinces as regards the availability of the Internet access among students in schools
of Silesia Province on the level of districts.
The last stage of the research was calculation of the Local Moran’s Ii statistics and in Table 4 statistics which were statistically significant were written in
boldface (with the level of significance 0.05). For the majority of districts the
Local Moran’s Ii statistics was statistically insignificant (0.05 < p-value < 0.95).
The p-value which is less than 0.05 indicates that the significant positive spatial
autocorrelation occurs, while the values higher than 0.95 indicate the existence
of significant negative local autocorrelation.
Table 4. Local Moran’s Ii statistics of Hellwig’s measure of development in 2012
District
częstochowski
gliwicki
m. Piekary Śląskie
będziński
pszczyński
m. Dąbrowa Górnicza
m. Bielsko-Biała
myszkowski
m. Tychy
m. Rybnik
tarnogórski
m. Chorzów
kłobucki
m. Gliwice
lubliniecki
m. Żory
mikołowski
cieszyński
Ii
0.498
-0.343
0.211
0.164
-0.281
-0.285
-0.088
0.855
0.062
0.142
0.186
-0.422
0.378
0.088
0.322
-0.035
-0.011
-0.009
p-value
0.085
0.811
0.321
0.211
0.745
0.674
0.524
0.031
0.417
0.321
0.245
0.832
0.216
0.425
0.211
0.455
0.485
0.470
District
bieruńsko-lędziński
m. Siemianowice Śl.
bielski
zawierciański
rybnicki
m. Katowice
żywiecki
m. Świętochłowice
wodzisławski
raciborski
m. Częstochowa
m. Bytom
m. Mysłowice
m. Jaworzno
m. Zabrze
m. Ruda Śląska
m. Sosnowiec
m. Jastrzębie-Zdrój
Ii
0.022
-0.913
-0.055
-0.131
-0.014
0.144
-0.062
0.524
0.225
-0.122
-0.855
-0.133
0.741
1.025
0.081
0.298
0.105
-0.388
p-value
0.421
0.523
0.520
0.576
0.458
0.221
0.517
0.124
0.354
0.544
0.881
0.595
0.056
0.033
0.355
0.166
0.375
0.814
From the data presented in Figure 4 and Table 4 it follows that in 2012 the
Local Moran’s Ii statistics was significant and higher than 0 only for districts such
as: myszkowski (0.855 p-value 0.031) and m. Jaworzno (1.025 p-value 0.033)
which indicates that those districts are surrounded by districts with significantly
similar values of the tested variable. Those districts are so called clusters.
The Use of Quantitative Methods…
133
3. The equipment of young people with modern means
of communication − the Internet addiction risk
According to the Local Data Bank of Polish CSO (GUS) research [www 4]
in 2014 74.8% of researched households in Poland possessed the Internet access
in home, with the reservation that the Internet access is more popular in households with children than in households without children. The use of the information and telecommunications technology is on the higher level in the younger
groups submitted to the research and it equals 94% of researched households
with the Internet access and with the children as the members of those households. It seems that young people are very particular population from the perspective of use of the information and telecommunications technology in everyday life. Local Data Bank of Polish CSO (GUS) research indicates that almost
the half of the researched 12-15 year olds accessed the Internet outside their
homes with the use of mobile phone or smartphone. The laptop computer was
used to access the Internet outside the house by almost 1/3 of researched young
people in age 12-15. The share of the people using the mobile tools to access the
Internet outside the house was higher in case of 12-15 year olds than in 16-74
year olds. In case of the mobile phones it was 18.9% higher and in case of the
laptop computers it was 9.1% higher. Smartphones were used by young people
mostly to listen to the music, to connect to social media and to play the games
where the share of 12-15 year olds using smartphones equals respectively to
26.8%, 26.3% and 23,5%. In the researched group, there is visible a disproportion as regards the sex in case of the use of the smartphone to play the games,
watch films and listen to the music (the percentage was higher in boys than in
girls respectively: 9.2%, 6.5% and 5.1%).
The main aim of the conducted author’s own research was the investigation
of the level of the equipment of Silesian young people between 13 and 29 years
old with modern means of communication with the access to the Internet and the
research with the use of K. Young’s screen tests − Internet Addiction Test (IAT)
to establish what percentage of Silesian young people is at the Internet addiction
risk and is addicted to the Web. The research was conducted with the use of self-administered survey on the purposefully chosen quota sample of young people
in several cities of Silesia Province (Katowice, Mysłowice, Zabrze, Jaworzno,
Sosnowiec) between December 2013 and May 2014. The surveyed young people
attended to secondary schools, upper secondary schools and universities. The
sample (after rejecting the surveys that were filled in incorrectly) encompassed
1037 people between 13 and 29 years old (including 319 university students, 470
upper secondary school students and 248 secondary school students).
Kattarzyna Warzeecha
134
The described
d
results are the part of the research
r
whiich is availabble among
other authhor’s studies [Warzecha, 2014a;
2
2014
4b; 2015a; 20015b; 2015c; 2015d].
The IAT test useed in the ressearch is onee of the mosst popular teests among
screen tessts employedd to researchh the Interneet addiction. Its high psyychometric
features are
a validated (the coefficiient of the test validationn − Cronbachh’s alpha is
high and it equals 0.889). The full description of the test can
c be foundd in studies
Young [19998]; Warzeccha [2014a].
As itt is shown inn Figure 5, the
t surveyed
d students arre quite welll equipped
with moddern means of
o communiccation but thee group withh the best equuipment is
upper seccondary schoool students.. The deskto
op computerr is availablee to about
80% of unniversity stuudents and uppper secondaary school sttudents and aabout 37%
of surveyyed secondarry school stuudents. Less than 90% of
o surveyed university
students, 80% of uppeer secondaryy school stud
dents and 43%
% of seconddary school
students owns
o
a laptoop computerr. About 77%
% of upper secondary sschool students, 70%
% of universsity studentss and about 32%
3
of secoondary schoool students
owns a mobile
m
phone.. Smartphonee was in possession of abbout 71% off university
students, over 61% of upper seecondary stu
udents and about
a
38% secondary
school stuudents, and thhe more andd more popullar among yooung people tablet was
the property of every third upper secondary school
s
studennt and of evvery fourth
secondaryy school studdent and univversity studen
nt.
89,3
80,2
78,4
90%
70,2
80%
7
78,7
76,8
71
61,3
60,9
70%
42,8
60%
40,6
36,,7
50%
23
31,8
37,3
31,1
22,5
40%
30%
17,6
20%
10%
0%
Yes-university sttudents
desktoop computer
laptoop computer
Y
Yes-secondary
school stu
udents
tabllet
mobile phonee with Internet
Yes-uppeer secondary school studeents
m
mobile
phone
smaartphone
Fig. 5. Thee equipment of
o Silesian youung people in modern meanns of communiication
accoording to schoool type (resullts in percentaage)
Source: Elabooration on the bassis of own researcch.
The Use of Quantitative Methods…
135
The results of Kimberly Young’s test – Internet Addiction test (IAT) of the
researched group of students between 13 and 29 years old are presented in Table 5
in general and with the division for the sex.
Table 5. The number and the percentage of students at risk and with no risk of the Internet
addiction, in general, according to school type and with the division for the sex1
Type of school
No risk group
Group at risk*
Total
N1
%
N2
%
N = N1 + N2
Young people in general
591
57.0
446
43.0
1037
Women
363
60.5
237
39.5
600
Men
228
52.2
209
47.8
437
University students
179
56.1
140
43.9
319
Secondary school students
148
59.7
100
40.3
248
Upper secondary school students
264
56.2
206
43.8
470
* The people who were addicted to the Internet constituted insignificant percentage of the surveyed people
(it was 2.8% in secondary schools: 6 women and 1 man, 2.8% in upper secondary schools: 8 women and
5 men, 2.5% in university students: 5 woman and 3 man) therefore they were classified in the further research and analyses as the people at risk of the Internet addiction.
Source: Elaboration based on own research, partial results available in studies of Warzecha [2014a].
On the basis of the general results obtained by Silesian young people in IAT
scale (Table 5) 28 people (2.7%2), from the 1037 people of researched students,
fulfilled − defined by Young – criteria of the Internet addiction, 446 people
(43%) fulfilled the criteria of being at risk with the addiction to the Web and 591
people (57%) were not at risk of the Internet addiction. As it is visible in author’s previous research (data presented in Table 5), the highest number of people at risk with the Internet addiction could be found among university students
(43.9% of people at risk of the Internet addiction and addicted to the Internet are
the university students), then there are upper secondary school students (43.8%
of people at risk of the Internet addiction and addicted to the Internet are the upper secondary school students) and at the end there are the secondary school students (40.3% of people at risk of the Internet addiction and addicted to the Internet are the secondary school students).
1
2
In the research of the Internet addiction with the use of Young’s IAT test various criteria of who
is qualified as a person pathologically using the Internet (addicted to the Internet) are assumed.
In the research, the division of K. Young was employed and described in the instruction to the
test realization [Young, 1998, p. 237-244].
The similar research results with the use of Young’s IAT test − 2.8% of surveyed people fulfilled the criteria of the Internet addiction, and 39.5% of surveyed people fulfilled the criteria
of being at the risk of the Internet addiction. Data on the basis of Pawłowska, Potembska [2011,
p. 439-442].
136
Katarzyna Warzecha
The general results of the test in IAT scale, obtained by the Silesian young
people with the division for sex, indicate that more men than women are at risk
of the Internet addiction (about the half of surveyed men (47.8%) was at risk of
the Internet addiction and addicted to the Internet, in comparison to 39.5%
of women who were at risk of the Internet addiction and addicted to the Web).
Conclusions
The computer with the Internet access, and in particular more and more
popular laptop computer and mobile phone or smartphone are nowadays the
most popular modern means of communication of contemporary young people.
The Internet, as an informational medium, helps young people in obtaining and
broadening the knowledge but at the same time, if inappropriately used, may
lead to addiction. That is why, the constant monitoring of young people’s Internet activity by their parents is so important.
The quantitative methods shown in this study are very useful tools to research the availability of the Internet access among young people in schools
(Hellwig’s method, Czekanowski’s diagram, methods of spatial statistics). According to the available subject literature the spatial methods are used more frequently in the analyses of economic and demographic processes [Zeug-Żebro et al.,
2014; Wolny-Dominiak, Zeug-Żebro, 2012, Warzecha, Wójcik, 2015e].
Hellwig’s taxonomic measure of development, which was used in the research, allowed the arrangement of Silesia Province districts as regards the availability of the Internet access among young people in schools and the creation of
a ranking of districts with schools equipped with computers in the highest degree.
With the use of Czekanowski’s method group of districts with similar level of the
availability of the Internet access among students in schools was identified.
On the basis of the current and the previous author’s research [Warzecha,
Wójcik, 2015e] it is possible to ascertain that over a span of 10 years (analyzed
years 2003, 2008, 2012) there is a significant change in the availability of the Internet access among young people in schools in Silesia Province. The worst conditions are invariably in Sosnowiec, Ruda Śląska, Jaworzno and Zabrze. The best
conditions are in częstochowski and gliwicki districts and in Piekary Śląskie.
When it comes to the estimation of the equipment of Silesian young people
with the modern means of communication it is clearly visible that upper secondary school students possess the best equipment, further on university students
and at the end secondary school students.
The Use of Quantitative Methods…
137
The results of K. Young’s screen tests indicate that being at risk of the
Internet addiction and being addicted to the Internet is the characteristic seen
more often at men’s group that in women’s group. Moreover, the highest number
of university students and upper secondary school students fulfilled the criteria
of being at risk and being addicted to the Web.
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The Use of Quantitative Methods…
139
ZASTOSOWANIE METOD ILOŚCIOWYCH W BADANIACH
WYBRANYCH UZALEŻNIEŃ BEHAWIORALNYCH MŁODZIEŻY
Streszczenie: Celem artykułu jest pokazanie zastosowania metod ilościowych w badaniach wybranych uzależnień behawioralnych młodzieży, a w szczególności w badaniu
patologicznego używania Internetu przez śląską młodzież. W badaniach wykorzystano
taksonomiczną miarę rozwoju Z. Hellwiga, która pozwoliła na uporządkowanie powiatów województwa śląskiego pod względem dostępności do Internetu młodzieży w szkołach i stworzenie rankingu powiatów najlepiej wyposażonych szkół w komputery. Zastosowanie metody Czekanowskiego pozwoliło na wyodrębnienie grup powiatów o podobnym
poziomie dostępności do Internetu uczniów w szkołach. W prowadzonych analizach sprawdzono także hipotezę, czy na dostępność do Internetu ma wpływ położenie terytorialne
badanych jednostek (w tym celu wykorzystano statystyki przestrzenne: mierniki lokalnej
i globalnej autokorelacji Morana). W badaniach opierano się na danych pochodzących
z Banku Danych Lokalnych GUS z 2012 oraz z badań własnych (ankietowych) przeprowadzonych w wybranych miastach województwa śląskiego. Uzależnienie od Internetu zbadano za pomocą testu K. Young – Internet Adiction Test. Obliczenia przeprowadzono z wykorzystaniem programu R Cran, pakietu Excel i SPSS.
Słowa kluczowe: uzależnienia behawioralne, Internet, młodzież, statystyki przestrzenne,
diagram Czekanowskiego, test K. Young.